Open AccessComputer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience
Author(s) -
Lukas Pfeifer,
Clemens Neufert,
Moritz Leppkes,
Maximilian J. Waldner,
Michael Häfner,
Albert Beyer,
Arthur Hoffman,
Peter D. Siersema,
Markus F. Neurath,
Timo Räth
Publication year - 2021
Publication title -
european journal of gastroenterology and hepatology
Language(s) - English
Resource type - Journals
eISSN - 1473-5687
pISSN - 0954-691X
DOI - 10.1097/meg.0000000000002209
Subject(s) - medicine , colonoscopy , convolutional neural network , adenoma , gold standard (test) , artificial intelligence , ex vivo , test set , colorectal polyp , in vivo , radiology , colorectal cancer , gastroenterology , computer science , cancer , microbiology and biotechnology , biology
The use of artificial intelligence represents an objective approach to increase endoscopist's adenoma detection rate (ADR) and limit interoperator variability. In this study, we evaluated a newly developed deep convolutional neural network (DCNN) for automated detection of colorectal polyps ex vivo as well as in a first in-human trial.